physrisk.vulnerability_models

Submodules

physrisk.vulnerability_models.chronic_heat_models

class physrisk.vulnerability_models.chronic_heat_models.ChronicHeatGZNModel(indicator_id: str = 'mean_degree_days/above/32c', delta=True)

Bases: VulnerabilityModelBase

Model which estiamtes the labour productivity impact based on chronic heat based on the paper “Neidell M, Graff Zivin J, Sheahan M, Willwerth J, Fant C, Sarofim M, et al. (2021) Temperature and work: Time allocated to work under varying climate and labor market conditions.” Average annual work hours are based on USA values reported by the OECD for 2021.

get_data_requests(asset: Asset, *, scenario: str, year: int) → HazardDataRequest | Sequence[HazardDataRequest]

Request the hazard data needed by the vulnerability model for a specific asset (this is a Google-style doc string)

Parameters:

  • asset – Asset for which data is requested.

  • scenario – Climate scenario of calculation.

  • year – Projection year of calculation.

Returns:

Single data requests.

get_impact(asset: Asset, data_responses: Sequence[HazardDataResponse]) → ImpactDistrib

Calcaulate impact (disruption) of asset based on the hazard data returned.

Parameters:

  • asset – Asset for which impact is calculated.

  • data_responses – responses to the hazard data requests generated in get_data_requests.

Returns:

Probability distribution of impacts.

class physrisk.vulnerability_models.chronic_heat_models.ChronicHeatWBGTGZNModel(indicator_id: str = 'mean_work_loss_high')

Bases: ChronicHeatGZNModel

Implementation of the WBGT/GZN chronic heat model. This model inherits attributes from the ChronicHeatGZN model and estimates the results based on applying both GZN and WBGT.

work_type_mapping()
get_data_requests(asset: Asset, *, scenario: str, year: int) → HazardDataRequest | Sequence[HazardDataRequest]

Request the hazard data needed by the vulnerability model for a specific asset (this is a Google-style doc string)

Parameters:

  • asset – Asset for which data is requested.

  • scenario – Climate scenario of calculation.

  • year – Projection year of calculation.

Returns:

Single or multiple data requests.

get_impact(asset: Asset, data_responses: Sequence[HazardDataResponse]) → ImpactDistrib

Function to return the impact distribution of the wbgt model.

physrisk.vulnerability_models.chronic_heat_models.two_variable_joint_variance(ex, varx, ey, vary)

Function to estimate the variance of two uncorrelated variables.

physrisk.vulnerability_models.chronic_heat_models.get_impact_distrib(fraction_loss_mean: float, fraction_loss_std: float, hazard_type: type, hazard_paths: List[str], impact_type: ImpactType, hazard_indicator_id: str) → ImpactDistrib

Calculate impact (disruption) of asset based on the hazard data returned.

Parameters:

  • fraction_loss_mean – mean of the impact distribution

  • fraction_loss_std – standard deviation of the impact distribution

  • hazard_type – Hazard Type.get_impact_distrib

  • impact_type – Impact Type.

  • hazard_paths – path to the hazard indicator data source.

Returns:

Probability distribution of impacts.

physrisk.vulnerability_models.example_models

class physrisk.vulnerability_models.example_models.ExampleCdfBasedVulnerabilityModel(*, indicator_id: str, hazard_type: type)

Bases: VulnerabilityModel

get_impact_curve(intensities, asset)

Define a VulnMatrixProvider.

The VulnMatrixProvider returns probabilities of specified impact bins for the intensity bin centres.

class physrisk.vulnerability_models.example_models.PlaceholderVulnerabilityModel(indicator_id: str, hazard_type: type, impact_type: ImpactType)

Bases: VulnerabilityModelBase

Placeholder vulnerability models are for when an impact or hazard indicator value is only intended to be used in a score-based risk measure.

get_data_requests(asset: Asset, *, scenario: str, year: int)

Provide the hazard event data requests.

It will return one or more HazardDataRequest as required to calculate the VulnerabilityDistrib and HazardEventDistrib for the asset.

get_impact(asset: Asset, data_responses: Sequence[HazardDataResponse]) → ImpactDistrib
class physrisk.vulnerability_models.example_models.PlaceholderFireModel

Bases: PlaceholderVulnerabilityModel

get_impact(asset: Asset, data_responses: Sequence[HazardDataResponse]) → ImpactDistrib

physrisk.vulnerability_models.labour_models

physrisk.vulnerability_models.power_generating_asset_models

class physrisk.vulnerability_models.power_generating_asset_models.InundationModel(indicator_id='flood_depth')

Bases: VulnerabilityModelAcuteBase

get_data_requests(asset: Asset, *, scenario: str, year: int) → HazardDataRequest | Sequence[HazardDataRequest]

Provide the list of hazard event data requests required in order to calculate the VulnerabilityDistrib and HazardEventDistrib for the asset.

get_distributions(asset: Asset, event_data_responses: Sequence[HazardDataResponse])

Return distributions for asset, based on hazard event date: VulnerabilityDistrib and HazardEventDistrib.

class physrisk.vulnerability_models.power_generating_asset_models.TemperatureModel

Bases: DeterministicVulnerabilityModel

class physrisk.vulnerability_models.power_generating_asset_models.SimpleDroughtModel

Bases: DeterministicVulnerabilityModel

physrisk.vulnerability_models.real_estate_models

Define vulnerability models for assessing the impact of various hazards on real estate assets.

class physrisk.vulnerability_models.real_estate_models.RealEstateInundationModel(*, hazard_type: type, indicator_id: str, resource: str = 'EU JRC global flood depth-damage functions', impact_bin_edges=array([0., 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]))

Bases: VulnerabilityModel

Inundation vulnerability model for real estates assets.

get_impact_curve(intensity_bin_centres: ndarray, asset: Asset) → VulnMatrixProvider

Build vulnerability distributions for flood intensities for a given asset.

Args:

intensity_bin_centres: One-dimensional array of hazard intensity bin

centres. Each value represents the midpoint of a discrete hazard intensity interval and is used to match intensity levels to the corresponding impacts from the vulnerability curve.

asset: Asset for which the vulnerability curve is required. When

asset is a RealEstateAsset, its location and type are used to select the appropriate curve. For a generic Asset, the fallback curve ("Europe", "Buildings/Residential") is used.

Returns:

VulnMatrixProvider: Provider containing one beta distribution-derived

impact CDF per intensity bin centre, constructed from interpolated impact means and standard deviations from the selected vulnerability curve, which defines the relationship between hazard intensity and impact.

closest_curve_of_type(curve: VulnerabilityCurve, asset_type: str) → VulnerabilityCurve

Find a proxy curve of the same asset type that includes standard deviations.

Args:

curve: Vulnerability curve whose mean impact profile is used as the

reference when searching for the closest proxy.

asset_type: Asset type of the asset under evaluation. It is used to

restrict the search to compatible vulnerability curves, for example "Buildings/Residential", so the selected proxy curve best matches that asset class.

Returns:

VulnerabilityCurve: The candidate curve of the requested asset type

with non-empty impact_std values and the smallest sum of squared differences from curve across the reference intensity grid.

sum_square_diff(curve1: VulnerabilityCurve, curve2: VulnerabilityCurve) → float

Compute a distance metric between two vulnerability curves.

Args:

curve1: Reference vulnerability curve. Its intensity grid is used as

the comparison domain.

curve2: Curve to compare against curve1. Its mean impacts are

interpolated onto the intensity values from curve1 before the difference is calculated.

Returns:

float: Sum of squared differences between curve1.impact_mean and

the interpolated curve2.impact_mean values. Smaller values indicate more similar curves.

class physrisk.vulnerability_models.real_estate_models.RealEstateCoastalInundationModel(*, indicator_id: str = 'flood_depth', resource: str = 'EU JRC global flood depth-damage functions', impact_bin_edges=array([0., 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]))

Bases: RealEstateInundationModel

Vulnerability model for assessing the impact of coastal inundation on real estate assets.

class physrisk.vulnerability_models.real_estate_models.RealEstatePluvialInundationModel(*, indicator_id: str = 'flood_depth', resource: str = 'EU JRC global flood depth-damage functions', impact_bin_edges=array([0., 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]))

Bases: RealEstateInundationModel

Pluvial inundation vulnerability model for real estate assets.

class physrisk.vulnerability_models.real_estate_models.RealEstateRiverineInundationModel(*, indicator_id: str = 'flood_depth', resource: str = 'EU JRC global flood depth-damage functions', impact_bin_edges=array([0., 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]))

Bases: RealEstateInundationModel

Riverine inundation vulnerability model for real estate assets.

class physrisk.vulnerability_models.real_estate_models.GenericTropicalCycloneModel

Bases: DeterministicVulnerabilityModel

Simple generic tropical cyclone vulnerability model for real estate assets.

wind_damage(v: ndarray, v_half: float)

Calculate damage based on functional form of Emanuel K. Global warming effects on US hurricane damage.

Weather, Climate, and Society. 2011 Oct 1;3(4):261-8. Using a threshold speed of 25.7 m/s. A review of the origin of parameters is available in Eberenz S, Lüthi S, Bresch DN. Regional tropical cyclone impact functions for globally consistent risk assessments. Natural Hazards and Earth System Sciences. 2021 Jan 29;21(1):393-415. which also provides suggested region-specific variations.

Args:

v (np.ndarray[float]): Wind speeds at which to calculate the fractional damage. v_half (float): The ‘v_half’ function parameter.

Returns:

np.ndarray[float]: Fractional damage.

class physrisk.vulnerability_models.real_estate_models.CoolingModel(threshold_temp_c: float = 23)

Bases: VulnerabilityModelBase

Simple degree-days-based vulnerability model for real estate assets.

get_data_requests(asset: Asset, *, scenario: str, year: int) → HazardDataRequest

Build the hazard data request needed to estimate cooling demand.

Args:

asset: Asset whose location and optional geometry define where hazard

parameter data should be requested.

scenario: Climate scenario identifier forwarded to the hazard model. year: Projection year for which the hazard parameter data is required.

Returns:

HazardDataRequest: Single request for chronic heat parameter data at

the asset coordinates, using this model’s indicator identifier and the asset geometry when available.

get_impact(asset: Asset, data_responses: Sequence[HazardDataResponse]) → ImpactDistrib

Module contents